Search

In recent years, evidence has accumulated towards a distractor suppression mechanism that enables efficient selection of targets in a visual search task. According to these findings, the search for a target is faster in the presence of a salient distractor in a display among homogenous distractors as opposed to its absence. Studies have also shown that distractor suppression not only operates on the feature level but can also be spatially guided. The motivation of the current study was to examine if spatially guided distractor suppression can be goal-driven. We tested this across four experiments. In Experiment 1A, the task was to search for a shape target (e.g., a circle) and discriminate the orientation of the line within it. In some trials, a salient color distractor was presented in the display while participants were told that it appeared in one of the two locations on the horizontal axis (or the vertical axis, counterbalanced across participants). We expected enhanced distractor suppression when the salient distractor appeared within this "spatial filter" but did not find it since the target was also presented at the filtered locations. Experiment 1B replicated Experiment 1A, except that the target was always presented outside the filter; filtering enhanced search performance. In Experiment 2 even when the filter contained the salient distractor in only 65% of the filtered trials, filtering benefited search performance. In Experiment 3, the filter changed on every trial and did not benefit suppression. [ABSTRACT FROM AUTHOR]

Due to the evolution of graphics processors over recent decades it has now become possible to produce high-quality, realistic three-dimensional scenes. However, aliasing occurs during the sampling process performed during rasterization, which causes a serrated effect around the edges of the objects presented in the scene, highlighting an unreal aspect of the image and causing displeasure to the viewer. This paper aims to develop an anti-aliasing treatment based on rotated spatial filtering. It undertakes edge detection by applying spatial filtering with a simple linear regression technique. A smoothing spatial filter is then rotated to match the direction of the inspected edge and is applied to the affected regions. Testing was performed on an OpenGL application, processing the rendered image from the framebuffer, and Blender, a 3D modeling software that enables scenes with more complex graphics. The results have demonstrated the effectiveness of the proposed method in smoothing aliasing with good quality while preserving the details of the scene. Hence, the problem was managed effectively with a post-filtering approach and without oversampling. The running time of the algorithm is O(n), and the memory used is O(n). [ABSTRACT FROM AUTHOR]

Aim: The continuous development of statistical tools applied to ecology has contributed to great advances for modelling species' niches and distributions from opportunistic observations. However, as these observations are subject to biases caused by spatial variation in sampling effort, ecological niche models (ENMs) are also frequently biased. Among several bias correction methods that have been proposed, spatial filtering—imposing a minimum distance between occurrences—is widely used, yet lacks clear guidelines for choosing the filtering distance. Here, we aimed to explore the impact of spatial filtering distances on the performance of ENMs. Location: Europe. Taxon: Virtual species. Methods: We applied ENMs to two virtual species with contrasting levels of specialisation, across a spectrum of modelling conditions, bias types and sample sizes. Results: Models applied to the specialist species had on average a lower performance than those applied to the generalist species. Using a biased sample reduced model performance, especially when the bias was strong, and when the sample size was large. In many cases, spatial filtering failed to improve model performance or even reduced it. We did find an improvement for the generalist species modelled with large and strongly biased datasets. However, there was no optimal filtering distance, as this improvement was linearly and positively associated with filtering distance. Moreover, because the initial bias was strong and the filtered dataset became very small, the resulting models had only very low accuracy. Main Conclusions: Our results suggest that there is no optimal filtering distance for dealing with sampling bias in ENMs, and that spatial filtering never improves model performance enough to draw accurate predictions. We therefore recommend spatial filtering to be employed cautiously, only when enough data are available, and bearing in mind that its effectiveness remains highly uncertain. [ABSTRACT FROM AUTHOR]

Reduced-order models (ROMs) have achieved a lot of success in reducing the computational cost of traditional numerical methods across many disciplines. In fluid dynamics, ROMs have been successful in providing efficient and relatively accurate solutions for the numerical simulation of laminar flows. For convection-dominated (e.g., turbulent) flows, however, standard ROMs generally yield inaccurate results, usually affected by spurious oscillations. Thus, ROMs are usually equipped with numerical stabilization or closure models in order to account for the effect of the discarded modes. The literature on ROM closures and stabilizations is large and growing fast. In this paper, instead of reviewing all the ROM closures and stabilizations, we took a more modest step and focused on one particular type of ROM closure and stabilization that is inspired by large eddy simulation (LES), a classical strategy in computational fluid dynamics (CFD). These ROMs, which we call LES-ROMs, are extremely easy to implement, very efficient, and accurate. Indeed, LES-ROMs are modular and generally require minimal modifications to standard ("legacy") ROM formulations. Furthermore, the computational overhead of these modifications is minimal. Finally, carefully tuned LES-ROMs can accurately capture the average physical quantities of interest in challenging convection-dominated flows in science and engineering applications. LES-ROMs are constructed by leveraging spatial filtering, which is the same principle used to build classical LES models. This ensures a modeling consistency between LES-ROMs and the approaches that generated the data used to train them. It also "bridges" two distinct research fields (LES and ROMs) that have been disconnected until now. This paper is a review of LES-ROMs, with a particular focus on the LES concepts and models that enable the construction of LES-inspired ROMs and the bridging of LES and reduced-order modeling. This paper starts with a description of a versatile LES strategy called evolve–filter–relax (EFR) that has been successfully used as a full-order method for both incompressible and compressible convection-dominated flows. We present evidence of this success. We then show how the EFR strategy, and spatial filtering in general, can be leveraged to construct LES-ROMs (e.g., EFR-ROM). Several applications of LES-ROMs to the numerical simulation of incompressible and compressible convection-dominated flows are presented. Finally, we draw conclusions and outline several research directions and open questions in LES-ROM development. While we do not claim this review to be comprehensive, we certainly hope it serves as a brief and friendly introduction to this exciting research area, which we believe has a lot of potential in the practical numerical simulation of convection-dominated flows in science, engineering, and medicine. [ABSTRACT FROM AUTHOR]

Diabetic Retinopathy and Glaucoma are two common diabetic retinal pathogens. It has the potential to damage human retina and result in vision impairment. Based on the International Diabetes Federation (IDF) reports, In India 77 million people were affected with diabetics 2019 and approximately 147.2 million people are expected by 2045. By effectively managing diabetes and undergoing routine eye examinations, would avoid or reduce the risk of eye complications such as diabetic retinopathy, cataracts, and Glaucoma. The Timely identification and precise delineation of affected areas in retinal pathogen images are crucial for effective disease management. To address this, we propose a novel hybrid diabetic retinal pathogen classification mechanism using Artificial Fish Swarm and Deep Convolutional Radial Basis Function network (AFS-DCRBF). This method utilizes retinal images containing normal, Diabetic-Retinopathy, and Glaucoma as inputs. Preprocessing of raw input images is performed using a spatial filtering technique in the initial phase. A vector-auto regression method is then employed for feature engineering, followed by retinal pathogen classification. The Deep-Convolutional Neural Network (DCNN) selects most informative traits from the extracted feature sequence, while the Radial Basis Function (RBF) module performs the classification task. The Artificial Fish Swarm (AFS) optimization fine-tunes the hyperparameters of DCRBF and improves classification performance. The proposed study was evaluated using ORIGA data set and publicly available datasets for DR. The proposed method required a total time of 1.12 seconds and achieved 99.40% accuracy and 99.61%specificity, and dice coefficient of 0.97. [ABSTRACT FROM AUTHOR]

A set of microphones spatially arranged in space in a specific pattern is called a microphone array. It can be used to extract and enhance the signal of interest from its observation corrupted by other interfering signals, such as noise or to estimate the direction of arrival of a source. In this paper we focus on a problem in which the desired signal (speech signal) is interfered by other signal with fully overlapping bandwidth but with different localization. Our goal is to attenuate the interfering signal. We experimentally study the method in which microphones do not have to be equally spaced and all information regarding signal phase is hidden in a transfer function of the microphone. We focus on determining the microphones positions and FIR filter coefficients so that the actual output the beamformer is as close as possible to the desired one (the level of speech signal remains unchanged, while the interfering signal is attenuated) in the sense of ���� norm. To solve this problem, we use a metaheuristic algorithm. Next, we construct the array and make an experiment in anechoic chamber. The initial results of the experiment show that the proposed method can be applied for array designing.

The next generation phased array radio telescopes, such as the Square Kilometre Array (SKA) low frequency aperture array, suffer from RF interference (RFI) because of the large field of view of antenna element. The classical station beamformer used in SKA-low is resource efficient but cannot deal with the unknown sidelobe RFI. A real-time adaptive beamforming strategy is proposed for SKA-low station, which trades the capability of adaptive RFI nulling at an acceptably cost, it doesn't require hardware redesign but only modifies the firmware accordingly. The proposed strategy uses a Parallel Least Mean Square (PLMS) algorithm, which has a computational complexity of 4 N + 2 and can be performed in parallel. Beam pattern and output SINR simulation results show deeply nulling performance to sidelobe RFI, as well as good mainlobe response similar to the classical beamformer. The convergence performance depends on the signal-and-interference environments and step size, wherein too large a step size leads to a non-optimal output SINR and too small a step size leads to slow convergence speed. FPGA implementation demonstrations are implemented and tested on a NI FPGA module, and test results demonstrate good real-time performance and low slice resource consumption. [ABSTRACT FROM AUTHOR]

Flow or collective movement is a frequently observed phenomenon for many cellular components including the cytoskeletal proteins actin and myosin. To study protein flow in living cells, we and others have previously used spatiotemporal image correlation spectroscopy (STICS) analysis on fluorescence microscopy image time series. Yet, in cells, multiple protein flows often occur simultaneously on different scales resulting in superimposed fluorescence intensity fluctuations that are challenging to separate using STICS. Here, we exploited the characteristic that distinct protein flows often occur at different spatial scales present in the image series to disentangle superimposed protein flow dynamics. We employed a newly developed and an established spatial filtering algorithm to alternatively accentuate or attenuate local image intensity heterogeneity across different spatial scales. Subsequently, we analysed the spatially filtered time series with STICS, allowing the quantification of two distinct superimposed flows within the image time series. As a proof of principle of our analysis approach, we used simulated fluorescence intensity fluctuations as well as time series of nonmuscle myosin II in endothelial cells and actin‐based podosomes in dendritic cells and revealed simultaneously occurring contiguous and noncontiguous flow dynamics in each of these systems. Altogether, this work extends the application of STICS for the quantification of multiple protein flow dynamics in complex biological systems including the actomyosin cytoskeleton. [ABSTRACT FROM AUTHOR]

Brain-computer interface (BCI) is a new promising technology for control and communication, the BCI system aims to decode the measured brain activity into a command signal. This paper proposes a hybrid approach to improve the classification performance of motor imagery BCI (MI BCI). Our proposed method aims to take the advantage of two principal feature extraction approaches. The first approach named Multi-Band common spatial patterns (MBCSP) consists of decomposing the MI trial into multiple sub-bands, for each sub-band CSP is applied to extract the features. Then, the subject-specific frequency bands are selected. Simultaneously, the selected frequency bands are used as input to the second approach named boosted tangent space mapping (BTSM), which extracts the features from each sub-band. An automatic feature selection algorithm is introduced to select the subject-specific frequency bands and to reduce the high dimensionality space of extracted features. Finally, the LogitBoost (LB) classifiers are learned on the extracted features by each approach and the linear combination of these classifiers is used to identify the class of MI trial. The proposed model is evaluated on three public MI-BCI datasets, including multiclass motor imagery datasets (BCI competition IV dataset 2a and BCI competition III dataset 3a) and binary class motor imagery dataset (BCI competition III dataset 4a), The average accuracy attained on the three datasets are respectively 73.61%, 86.66%, and 86.68%. The conducted comparative study between the proposed approach and some state-of-the-the art methods showed a statistically significant improvement (p-value < 0.05) of the classification performance of MI BCI. The experiment results showed a significant improvement when using the proposed hybrid approach against the use of a single-feature extraction method. [ABSTRACT FROM AUTHOR]

We consider a class of high‐dimensional spatial filtering problems, where the spatial locations of observations are unknown and driven by the partially observed hidden signal. This problem is exceptionally challenging, as not only is it high‐dimensional, but the model for the signal yields longer‐range time dependences through the observation locations. Motivated by this model, we revisit a lesser‐known and provably convergent computational methodology from Berzuini et al. (1997, Journal of the American Statistical Association, 92, 1403–1412); Centanniand Minozzo (2006, Journal of the American Statistical Association, 101, 1582–1597); Martin et al. (2013, Annals of the Institute of Statistical Mathematics, 65, 413–437) that uses sequential Markov Chain Monte Carlo (MCMC) chains. We extend this methodology for data filtering problems with unknown observation locations. We benchmark our algorithms on linear Gaussian state‐space models against competing ensemble methods and demonstrate a significant improvement in both execution speed and accuracy. Finally, we implement a realistic case study on a high‐dimensional rotating shallow‐water model (of about 104$$ 1{0}^4 $$–105$$ 1{0}^5 $$ dimensions) with real and synthetic data. The data are provided by the National Oceanic and Atmospheric Administration (NOAA) and contain observations from ocean drifters in a domain of the Atlantic Ocean restricted to the longitude and latitude intervals [−51∘,−41∘]$$ \left[-5{1}^{\circ },-4{1}^{\circ}\right] $$, [17∘,27∘]$$ \left[1{7}^{\circ },2{7}^{\circ}\right] $$, respectively. [ABSTRACT FROM AUTHOR]

We propose a method for multiple-depth extraction in diffraction grating imaging. A diffraction grating can optically generate a diffraction image array (DIA) having parallax information about a three-dimensional (3D) object. The optically generated DIA has the characteristic of forming images periodically, and the period depends on the depth of the object, the wavelength of the light source, and the grating period of the diffraction grating. The depth image can be extracted through the convolution of the DIA and the periodic delta function array. Among the methods for extracting depth images through the convolution characteristics of a parallax image array (PIA) and delta function array, an advanced spatial filtering method for the controllable extract of multiple depths (CEMD) has been studied as one of the reconstruction methods. And that possibility was confirmed through a lens-array-based computational simulation. In this paper, we aim to perform multiple-depth extraction by applying the CEMD method to a DIA obtained optically through a diffraction grating. To demonstrate the application of the CEMD in diffraction grating imaging, a theoretical analysis is performed to apply the CEMD in diffraction grating imaging; the DIA is acquired optically, and the spatial filtering process is performed through computational methods and then compared with the conventional single-depth extraction method in diffraction grating imaging. The application of the CEMD to DIA enables the simultaneous reconstruction of images corresponding to multiple depths through a single spatial filtering process. To the best of our knowledge, this is the first research on the extraction of multiple-depth images in diffraction grating imaging. [ABSTRACT FROM AUTHOR]

The employment of Massive MIMO technology in 5G communication results in a large number of RF channels in the base station. Therefore, the base station will suffer from the huge burden of equipping discrete bandpass filters under the traditional filtering strategy. An alternative filtering strategy called spatial filtering characterizes direct covering frequency selective surface (FSS) on multi-channel antenna array of base station and is expected to greatly relax the need for discrete bandpass filters. An aperture-coupled-cross-resonator (ACCR) FSS based spatial filtering scheme is investigated in this study. A low-profile spatial filtering patch array (LPSFPA) with thickness of $0.07\lambda _{0}$ and operating around 3.6GHz is constructed by placing an asymmetrical ACCR FSS tightly upon a patch antenna array with a half-period lateral shift. A good out-of-band suppression at both band-edges and enhanced in-band impedance bandwidth are achieved when the main beam is scanned with an elevation angle range of 0° to 30° and arbitrary azimuth angle. To verify the design, a finite LPSFPA sample whose main beam can be directed to $\theta _{\mathrm {b}}= 0^{\mathrm {\circ }}$ , $15^{\mathrm {\circ }}$ and $30^{\mathrm {\circ }}$ in E plane by different phasing and power-dividing networks is fabricated and tested. The measured results show that the fabricated LPSFPA sample is superior to the ordinary radiation patch array without the ACCR FSS in terms of both the impedance bandwidth and out-of-band suppression for various main beam directions. For the normal-beam case, the measured −10dB impedance bandwidth is from 3.6-3.8 GHz; the measured out-of-band suppression level of peak realized gain is more than 19 dB in the lower band-edge from 3.2-3.5 GHz and 17 dB in the upper band-edge from 3.9-4.2 GHz. The proposed spatial filtering scheme has no negative influence on the radiation performance of the radiation patch array.

Abstract In the civil structural health monitoring fields, monitored data suffer from noise and sensor faults. In practice, redundant sensors are usually deployed to monitor structural condition to obtain more accurate and robust information. This paper proposes a beamforming-based spatial filtering method to improve the data quality by using the information redundancy within sensor networks. Data pre-processing is first implemented, including missing data imputation and thermal response separation. Subsequently, short-term Fourier transform is used to transform the measured time sequences into time–frequency domain to obtain more useful features. Finally, signals in the time and frequency domain are processed using the beamforming algorithm. In the beamformers, a linear filter is applied to suppress noise signals, which is formulated as a constrained optimization problem. Herein, interior point algorithm is used to optimize the allocation of the linear filter, wherein the objective function is to minimize the power of the noise component at the beamformer output. The effectiveness of the proposed method is verified by using signals from strain gauges installed on steel deck plates of the 3rd Nanjing Yangtze River Bridge. Results through the case study show that signals after spatial filtering have a satisfactory de-noising, which indicates the effectiveness of the proposed beamforming algorithm. We believe that the proposed beamforming algorithm has substantial potential applications, such as providing high quality data source for further investigations.

Reduced-order models (ROMs) have achieved a lot of success in reducing the computational cost of traditional numerical methods across many disciplines. In fluid dynamics, ROMs have been successful in providing efficient and relatively accurate solutions for the numerical simulation of laminar flows. For convection-dominated (e.g., turbulent) flows, however, standard ROMs generally yield inaccurate results, usually affected by spurious oscillations. Thus, ROMs are usually equipped with numerical stabilization or closure models in order to account for the effect of the discarded modes. The literature on ROM closures and stabilizations is large and growing fast. In this paper, instead of reviewing all the ROM closures and stabilizations, we took a more modest step and focused on one particular type of ROM closure and stabilization that is inspired by large eddy simulation (LES), a classical strategy in computational fluid dynamics (CFD). These ROMs, which we call LES-ROMs, are extremely easy to implement, very efficient, and accurate. Indeed, LES-ROMs are modular and generally require minimal modifications to standard (“legacy”) ROM formulations. Furthermore, the computational overhead of these modifications is minimal. Finally, carefully tuned LES-ROMs can accurately capture the average physical quantities of interest in challenging convection-dominated flows in science and engineering applications. LES-ROMs are constructed by leveraging spatial filtering, which is the same principle used to build classical LES models. This ensures a modeling consistency between LES-ROMs and the approaches that generated the data used to train them. It also “bridges” two distinct research fields (LES and ROMs) that have been disconnected until now. This paper is a review of LES-ROMs, with a particular focus on the LES concepts and models that enable the construction of LES-inspired ROMs and the bridging of LES and reduced-order modeling. This paper starts with a description of a versatile LES strategy called evolve–filter–relax (EFR) that has been successfully used as a full-order method for both incompressible and compressible convection-dominated flows. We present evidence of this success. We then show how the EFR strategy, and spatial filtering in general, can be leveraged to construct LES-ROMs (e.g., EFR-ROM). Several applications of LES-ROMs to the numerical simulation of incompressible and compressible convection-dominated flows are presented. Finally, we draw conclusions and outline several research directions and open questions in LES-ROM development. While we do not claim this review to be comprehensive, we certainly hope it serves as a brief and friendly introduction to this exciting research area, which we believe has a lot of potential in the practical numerical simulation of convection-dominated flows in science, engineering, and medicine.

Introduction: One of the primary motivations for studying the human brain is to comprehend how external sensory input is processed and ultimately perceived by the brain. A good understanding of these processes can promote the identification of biomarkers for the diagnosis of various neurological disorders; it can also provide ways of evaluating therapeutic techniques. In this work, we seek the minimal requirements for identifying key stages of activity in the brain elicited by median nerve stimulation. Methods: We have used a priori knowledge and applied a simple, linear, spatial filter on the electroencephalography and magnetoencephalography signals to identify the early responses in the thalamus and cortex evoked by short electrical stimulation of the median nerve at the wrist. The spatial filter is defined first from the average EEG and MEG signals and then refined using consistency selection rules across ST. The refined spatial filter is then applied to extract the timecourses of each ST in each targeted generator. These ST timecourses are studied through clustering to quantify the ST variability. The nature of ST connectivity between thalamic and cortical generators is then studied within each identified cluster using linear and non-linear algorithms with time delays to extract linked and directional activities. A novel combination of linear and non-linear methods provides in addition discrimination of influences as excitatory or inhibitory. Results: Our method identifies two key aspects of the evoked response. Firstly, the early onset of activity in the thalamus and the somatosensory cortex, known as the P14 and P20 in EEG and the secondM20 forMEG. Secondly, good estimates are obtained for the early timecourse of activity from these two areas. The results confirm the existence of variability in ST brain activations and reveal distinct and novel patterns of connectivity in di [ABSTRACT FROM AUTHOR]

In the civil structural health monitoring fields, monitored data suffer from noise and sensor faults. In practice, redundant sensors are usually deployed to monitor structural condition to obtain more accurate and robust information. This paper proposes a beamforming-based spatial filtering method to improve the data quality by using the information redundancy within sensor networks. Data pre-processing is first implemented, including missing data imputation and thermal response separation. Subsequently, short-term Fourier transform is used to transform the measured time sequences into time–frequency domain to obtain more useful features. Finally, signals in the time and frequency domain are processed using the beamforming algorithm. In the beamformers, a linear filter is applied to suppress noise signals, which is formulated as a constrained optimization problem. Herein, interior point algorithm is used to optimize the allocation of the linear filter, wherein the objective function is to minimize the power of the noise component at the beamformer output. The effectiveness of the proposed method is verified by using signals from strain gauges installed on steel deck plates of the 3rd Nanjing Yangtze River Bridge. Results through the case study show that signals after spatial filtering have a satisfactory de-noising, which indicates the effectiveness of the proposed beamforming algorithm. We believe that the proposed beamforming algorithm has substantial potential applications, such as providing high quality data source for further investigations. [ABSTRACT FROM AUTHOR]

Polarimetric Synthetic Aperture Radar (PolSAR) data is inherently characterized by speckle noise, which significantly deteriorates certain aspects of the quality of the PolSAR data processing, including the polarimetric decomposition and target interpretation. With the rapid increase in PolSAR resolution, SAR images in complex natural and artificial scenes exhibit non-homogeneous characteristics, which creates an urgent demand for high-resolution PolSAR filters. To address these issues, a new adaptive PolSAR filter based on joint similarity measure criterion (JSMC) is proposed in this paper. Firstly, a scale-adaptive filtering window is established in order to preserve the texture structure based on a multi-directional ratio edge detector. Secondly, the JSMC is proposed in order to accurately select homogeneous pixels; it describes pixel similarity based on both space distance and polarimetric distance. Thirdly, the homogeneous pixels are filtered based on statistical averaging. Finally, the airborne and spaceborne real data experiment results validate the effectiveness of our proposed method. Compared with other filters, the filter proposed in this paper provides a better outcome for PolSAR data in speckle suppression, edge texture, and the preservation of polarimetric properties. [ABSTRACT FROM AUTHOR]

The next generation phased array radio telescopes, such as the Square Kilometre Array (SKA) low frequency aperture array, suffer from RF interference (RFI) because of the large field of view of antenna element. The classical station beamformer used in SKA-low is resource efficient but cannot deal with the unknown sidelobe RFI. A real-time adaptive beamforming strategy is proposed for SKA-low station, which trades the capability of adaptive RFI nulling at an acceptably cost, it doesn’t require hardware redesign but only modifies the firmware accordingly. The proposed strategy uses a Parallel Least Mean Square (PLMS) algorithm, which has a computational complexity of 4N+2 and can be performed in parallel. Beam pattern and output SINR simulation results show deeply nulling performance to sidelobe RFI, as well as good mainlobe response similar to the classical beamformer. The convergence performance depends on the signal-and-interference environments and step size, wherein too large a step size leads to a non-optimal output SINR and too small a step size leads to slow convergence speed. FPGA implementation demonstrations are implemented and tested on a NI FPGA module, and test results demonstrate good real-time performance and low slice resource consumption.

Machine vision-based multi-degree-of-freedom laser cladding system integration and process monitoring is an important methods to ensure high-quality laser cladding. In this paper, based on the basic theory of multi-degree of freedom of machine vision, the laser cladding system is redesigned, and the melt pool images during the cladding process are monitored and analyzed. The melt pool image is pre-processed with the help of spatial filtering, and the image is thresholded using the iterative method thresholding to analyze the melt pool image. It is proved that the larger the laser power is, the larger the melt pool area is, and the maximum melt pool area is 60,000 pix when the laser power is 200 W. The maximum melt pool area is achieved when the nozzle tilt angle is 30° and the melt pool area is vertical. The smaller the melting speed, the larger the melting area and the maximum melting pool area is up to 35000pix when the melting speed is 210mm/min. It shows that the integration and process monitoring of the multi-degree-of-freedom laser melting system based on machine vision can reasonably control the process parameters of laser melting and improve the quality of laser melting by real-time monitoring and analysis of the melting pool images.

IntroductionOne of the primary motivations for studying the human brain is to comprehend how external sensory input is processed and ultimately perceived by the brain. A good understanding of these processes can promote the identification of biomarkers for the diagnosis of various neurological disorders; it can also provide ways of evaluating therapeutic techniques. In this work, we seek the minimal requirements for identifying key stages of activity in the brain elicited by median nerve stimulation.MethodsWe have used a priori knowledge and applied a simple, linear, spatial filter on the electroencephalography and magnetoencephalography signals to identify the early responses in the thalamus and cortex evoked by short electrical stimulation of the median nerve at the wrist. The spatial filter is defined first from the average EEG and MEG signals and then refined using consistency selection rules across ST. The refined spatial filter is then applied to extract the timecourses of each ST in each targeted generator. These ST timecourses are studied through clustering to quantify the ST variability. The nature of ST connectivity between thalamic and cortical generators is then studied within each identified cluster using linear and non-linear algorithms with time delays to extract linked and directional activities. A novel combination of linear and non-linear methods provides in addition discrimination of influences as excitatory or inhibitory.ResultsOur method identifies two key aspects of the evoked response. Firstly, the early onset of activity in the thalamus and the somatosensory cortex, known as the P14 and P20 in EEG and the second M20 for MEG. Secondly, good estimates are obtained for the early timecourse of activity from these two areas. The results confirm the existence of variability in ST brain activations and reveal distinct and novel patterns of connectivity in different clusters.DiscussionIt has been demonstrated that we can extract new insights into stimulus processing without the use of computationally costly source reconstruction techniques which require assumptions and detailed modeling of the brain. Our methodology, thanks to its simplicity and minimal computational requirements, has the potential for real-time applications such as in neurofeedback systems and brain-computer interfaces.

This paper presents a comparative benchmarking of different beamforming techniques for robust multipath and spoofing mitigation in handheld receivers equipped with a small number of antenna elements. In the field of Global Navigation Satellite Systems (GNSSs), multipath and spoofing have become major sources of signal degradation, whereby the presence of closely spaced replicas of the signal of interest hampers the receiver's performance. Spatial filtering through the use of antenna arrays is one appealing approach to combat these effects. However, the upward trend in device miniaturization poses a serious concern for spatial filtering in handheld devices such as tablets or smartphones, particularly for the case of decimeter-wave signals such as in GNSSs. The terminal size constraints lead to deploying arrays with very few antennas, thus degrading the mitigation performance of the above impairments. This work explores the feasibility of performing robust multipath and spoofing mitigation in GNSS receivers with antenna arrays of very small sizes. Simulation results on four-antenna rectangular and two-antenna linear array distributions are provided, showing that the reduction in array size can be counteracted by the use of advanced beamformers. [ABSTRACT FROM AUTHOR]

Hyperspectral anomaly detection (HAD) is an important application of hyperspectral images (HSI) that can distinguish anomalies from background in an unsupervised manner. As a common unsupervised network in deep learning, autoencoders (AE) have been widely used in HAD and can highlight anomalies by reconstructing the background. This study proposed a novel spatial–spectral joint HAD method based on a two-branch 3D convolutional autoencoder and spatial filtering. We used the two-branch 3D convolutional autoencoder to fully extract the spatial–spectral joint features and spectral interband features of HSI. In addition, we used a morphological filter and a total variance curvature filter for spatial detection. Currently, most of the datasets used to validate the performance of HAD methods are airborne HSI, and there are few available satellite-borne HSI. For this reason, we constructed a dataset of satellite-borne HSI based on the GF-5 satellite for experimental validation of our anomaly detection method. The experimental results for the airborne and satellite-borne HSI demonstrated the superior performance of the proposed method compared with six state-of-the-art methods. The area under the curve (AUC) values of our proposed method on different HSI reached above 0.9, which is higher than those of the other methods. [ABSTRACT FROM AUTHOR]

This study presents a new estimate for subsidence in the northeast of the country through the time series analysis of 11-year (from the beginning of 2006 till the end of 2016) of 31 stations of the Khorasan network, as part of the Iranian Permanent Geodynamic & GNSS Network (IPGN), located in northeastern Iran. The mentioned estimation is obtained from the velocity vector of network stations in the International Terrestrial Reference Frame of ITRF2014 based on time series analysis in two realms, i.e., deterministic model analysis and stochastic model analysis. The deterministic model analysis is comprised of jump detection, determination of station motion model parameters, the study of station trend, outlier detection, and statistical significance test to check the jumps magnitude. Due to the interdependence of these steps, the related calculations are performed iteratively. Noise analysis includes two phases, namely, spatial filtering and temporal filtering. In the first phase, the Common Mode Error (CME) parameter is calculated using the weighted stacking method and taking into account the data correlation coefficient and stations distance. In the second phase, using the maximum likelihood estimation (MLE) method, the optimal noise model is derived as a combination of white noise and flicker noise. As a result, the reliable velocities of the stations (resulting from a complete analysis of the deterministic model) and their realistic uncertainties (resulting from the selection of optimal stochastic models) are calculated. Based on this study we found that: (1) Each station during the 11-year study period has on average nine jumps, all of which are of non-seismic origin. (2) Including the data from all IPGN stations in spatial filtering, leads to better results and on average reduces the norm of post-fit residual vectors for east, north, and up coordinate components by 30.17%, 29.40%, and 17.90%, respectively. (3) Concerning the temporal filtering, we found that the noise of the up-component is significantly higher than the noise of the horizontal components. (4) Stochastic model analysis showed the realistic uncertainties of the east, north, and up components are 4.33, 4.44, and 3.70 times, respectively, greater than the uncertainties which are derived without application of stochastic modeling (optimistic uncertainties). (5) The vertical velocity of most of stations was found to be in the normal range of -5 to 5 mm/yr. (6) Five stations, namely, GOLM, GRGN, NFRD, NISH, and SHRN are having anomalous subsidence (up to 9 mm/yr). (7) The proximity of the three stations GOLM, NFRD, and NISH allows us to infer a regional subsidence for the area of their location. (8) The station GRGN, in addition to anomalous subsidence, shows distinctive features such as the nonlinear trend as well as large periodic signals in the up component of the station. Therefore, to find the reason for such vertical behavior of the earth's crust more permanent GNSS stations must be established in that area. (9) The estimated parameters of periodic signal of the stations demonstrate that the annual and draconitic year signals have the largest amplitudes in the three coordinate components. In addition, amplitude of the periodic signals of the up component is significantly larger than the other components. [ABSTRACT FROM AUTHOR]

Brain biometrics have received increasing attention from the scientific community due to their unique properties compared to traditional biometric methods. Many studies have shown that EEG features are distinct across individuals. In this study, we propose a novel approach by considering spatial patterns of the brain's responses due to visual stimulation at specific frequencies. More specifically, we propose, for the identification of the individuals, to combine common spatial patterns with specialized deep-learning neural networks. The adoption of common spatial patterns gives us the ability to design personalized spatial filters. In addition, with the help of deep neural networks, the spatial patterns are mapped into new (deep) representations where the discrimination between individuals is performed with a high correct recognition rate. We conducted a comprehensive comparison between the performance of the proposed method and several classical methods on two steady-state visual evoked potential datasets consisting of thirty-five and eleven subjects, respectively. Furthermore, our analysis includes a large number of flickering frequencies in the steady-state visual evoked potential experiment. Experiments on these two steady-state visual evoked potential datasets showed the usefulness of our approach in terms of person identification and usability. The proposed method achieved an averaged correct recognition rate of 99% over a large number of frequencies for the visual stimulus. [ABSTRACT FROM AUTHOR]

New GNSS applications demand resilience against radio interference and high position accuracy. Separately, these demands can be fulfilled by multi-antenna systems using spatial filtering and carrier-phase positioning algorithms like real-time kinematic (RTK), respectively. However, combining these approaches encounters a severe issue: The spatial filtering induces a phase offset into the measured carrier phase leading to a loss of position accuracy. This paper presents a new approach to compensate for the phase offset in a blind manner, (i.e., without knowing the antenna array radiation pattern or the direction of arrival of the signals). The proposed approach is experimentally validated in two jamming scenarios. One includes a jammer with increasing power and the other includes a moving jammer. The results demonstrate that the approach successfully compensates for the phase offset and, hence, allows for the combined use of RTK positioning and spatial filtering even under jamming. [ABSTRACT FROM AUTHOR]

Digital holographic microscopy has significant advantages over traditional 3D measurements. In order to obtain a reconstructed image of superior quality, the zero-order-term spectrum and the −1 term spectrum must be eliminated during the digital reconstruction. At the same time, digital holographic microscopy has changed from the traditional manual selection of the +1 term spectrum to the automatic selection of the +1 term spectrum. In the process of spatial filtering of the +1 term spectrum, the filtering window is crucial, and the shape of the filtering window and the robustness of the filtering window to noise will ultimately affect the reconstruction results. In this paper, we propose an automatic spatial filtering method that combines wavelet transform and mathematical morphology to automatically extract the +1 term spectrum. Compared with conventional methods, the reconstruction results are more accurate and robust to noise, and the experimental results verify the feasibility of the proposed method. [ABSTRACT FROM AUTHOR]

Polarimetric Synthetic Aperture Radar (PolSAR) data is inherently characterized by speckle noise, which significantly deteriorates certain aspects of the quality of the PolSAR data processing, including the polarimetric decomposition and target interpretation. With the rapid increase in PolSAR resolution, SAR images in complex natural and artificial scenes exhibit non-homogeneous characteristics, which creates an urgent demand for high-resolution PolSAR filters. To address these issues, a new adaptive PolSAR filter based on joint similarity measure criterion (JSMC) is proposed in this paper. Firstly, a scale-adaptive filtering window is established in order to preserve the texture structure based on a multi-directional ratio edge detector. Secondly, the JSMC is proposed in order to accurately select homogeneous pixels; it describes pixel similarity based on both space distance and polarimetric distance. Thirdly, the homogeneous pixels are filtered based on statistical averaging. Finally, the airborne and spaceborne real data experiment results validate the effectiveness of our proposed method. Compared with other filters, the filter proposed in this paper provides a better outcome for PolSAR data in speckle suppression, edge texture, and the preservation of polarimetric properties.

Processing spatially distributed nociceptive information is critical for survival. The conditioned pain modulation (CPM) response has become a common psychophysical test to examine pain modulation capabilities related to spatial filtering of nociceptive information. Neuroimaging studies have been conducted to elucidate the neural mechanisms underlying the CPM response in health and chronic pain states, yet their findings have not been critically reviewed and synthesized before. This narrative review presents a simplified overview of MRI methodology in relation to CPM assessments and summarizes the findings of neuroimaging studies on the CPM response. The summary includes functional MRI studies assessing CPM responses during scanning as well as functional and structural MRI studies correlating indices with CPM responses assessed outside of the scanner. The findings are discussed in relation to the suggested mechanisms for the CPM response. A better understanding of neural mechanisms underlying spatial processing of nociceptive information could advance both pain research and clinical use of the CPM response as a marker or a treatment target. [ABSTRACT FROM AUTHOR]

Thermal imaging plays a vital role in structural health monitoring of various materials and provides insight into the defect present due to aging, deterioration, and fault during construction. This study investigated the effectiveness of spatial filters during pre-processing of thermal images and a correlation technique in post-processing, as well as exploited its application in non-destructive testing and evaluation of defects in steel structures. Two linear filters (i.e., Gaussian and Window Averaging) and a non-linear filter (i.e., Median) were implemented during pre-processing of a pulsed thermography image sequence. The effectiveness of implemented filters was then assessed using signal to noise ratio as a quality metric. The result of pre-processing revealed that each implemented filter is capable of reducing impulse noise and producing high-quality images; additionally, when comparing the signal to noise ratio, the Gaussian filter dominated both Window Averaging and Median filters. Defect size was determined using a correlation technique on a sequence of pulsed thermography images that had been pre-processed with a Gaussian filter. Finally, it is concluded that the correlation technique could be applied to the fast measurement of defect size, even though the accuracy may depend on the detection limit of thermography and defect size to depth ratio. [ABSTRACT FROM AUTHOR]

In this paper, we examine the applicability limits of different methods of compensation of the individual properties of self-emitting displays with significant non-uniformity of chromaticity and maximum brightness. The aim of the compensation is to minimize the perceived image non-uniformity. Compensation of the displayed image non-uniformity is based on minimizing the perceived distance between the target (ideally displayed) and the simulated image displayed by the calibrated screen. The S-CIELAB model of the human visual system properties is used to estimate the perceived distance between two images. In this work, we compare the efficiency of the channel-wise and linear (with channel mixing) compensation models depending on the models of variation in the characteristics of display elements (subpixels). It was found that even for a display with uniform chromatic subpixels characteristics, the linear model with channel mixing is superior in terms of compensation accuracy.

Population spatialization is widely used for spatially downscaling census population data to finer-scale. The core idea of modern population spatialization is to establish the association between ancillary data and population at the administrative-unit-level (AU-level) and transfer it to generate the gridded population. However, the statistical characteristic of attributes at the pixel-level differs from that at the AU-level, thus leading to prediction bias via the cross-scale modeling (i.e. scale mismatch problem). In addition, integrating multi-source data simply as covariates may underutilize spatial semantics, and lead to incorrect population disaggregation; while neglecting the spatial autocorrelation of population generates excessively heterogeneous population distribution that contradicts to real-world situation. To address the scale mismatch in downscaling, this paper proposes a Cross-Scale Feature Construction (CSFC) method. More specifically, by grading pixel-level attributes, we construct the feature vector of pixel grade proportions to narrow the scale differences in feature representation between AU-level and pixel-level. Meanwhile, fine-grained building patch and mobile positioning data are utilized to adjust the population weighting layer generated from POI-density-based regression modeling. Spatial filtering is furtherly adopted to model the spatial autocorrelation effect of population and reduce the heterogeneity in population caused by pixel-level attribute discretization. Through the comparison with traditional feature construction method and the ablation experiments, the results demonstrate significant accuracy improvements in population spatialization and verify the effectiveness of weight correction steps. Furthermore, accuracy comparisons with WorldPop and GPW datasets quantitatively illustrate the advantages of the proposed method in fine-scale population spatialization.

This paper presents a comparative benchmarking of different beamforming techniques for robust multipath and spoofing mitigation in handheld receivers equipped with a small number of antenna elements. In the field of Global Navigation Satellite Systems (GNSSs), multipath and spoofing have become major sources of signal degradation, whereby the presence of closely spaced replicas of the signal of interest hampers the receiver’s performance. Spatial filtering through the use of antenna arrays is one appealing approach to combat these effects. However, the upward trend in device miniaturization poses a serious concern for spatial filtering in handheld devices such as tablets or smartphones, particularly for the case of decimeter-wave signals such as in GNSSs. The terminal size constraints lead to deploying arrays with very few antennas, thus degrading the mitigation performance of the above impairments. This work explores the feasibility of performing robust multipath and spoofing mitigation in GNSS receivers with antenna arrays of very small sizes. Simulation results on four-antenna rectangular and two-antenna linear array distributions are provided, showing that the reduction in array size can be counteracted by the use of advanced beamformers.

Purpose of this work is to determine regularities of formation of spatial structures in an ensemble of chaotic systems with non-local diffusion couplings and to study how these structures depend on the wave response of the digital filter formed by the ensemble couplings structure. Methods. The study was carried out by numerical simulation of an ensemble of logistic maps, calculation of its typical oscillatory regimes and their spectral analysis. The network was considered as a digital filter with a frequency response depending on the coupling parameters. Correlation between the spatial spectra and the amplitude-frequency response of the coupling filter and the mutual coherence of oscillations when the coupling parameters change were considered. Results. The system of couplings between chaotic maps behaves like a wave filter with selective properties, allowing spatial modes with certain wavelengths to exist and suppressing others. The selection of spatial modes is based on the wave characteristic of the coupling filter, the type of which is determined by the radius and the magnitude of couplings. At strong coupling the wave characteristics for ensembles with local and non-local couplings are qualitatively different, therefore they demonstrate essencially different behavior. Discussion. Using spectral methods for dynamics analysis systems with complex network topologies seems to be a promising approach, especially for research of synchronization and multistability in ensembles of chaotic oscillators and maps. The found regularities generalize the results known for ensembles of maps with local couplings. They also can be applied to ensembles of self-sustained oscillators.

Small target detection in high strength clutter background is in great in remote imaging system, a new improved spatio-temporal filtering was proposed in this paper. Firstly, traditional anisotropy filtering has poor suppression effect in strength edge contour region, so a new diffusion filtering function proposed in paper. According to the analysis with difference of each component of the image, a new anisotropy diffusion function is constructed in this paper. When the difference of background and target is small, this algorithm will give in large diffusion coefficient to filter most background clutter and retain target signal well which achieves background prediction better. Secondly, because the traditional spatiotemporal filter algorithm cant follow the motion object in the fixed search pipe diameter what will make lose the target detection, a new weight constraint function of adaptive change of the search diameter in this paper is built which can change the search diameter with the moving of target, and improve the detection accuracy. Finally, experiments show that compared with traditional algorithms and detected in different scenes, this method can enhance small target detection effectively.

Noise pollution is one of the major health risks in urban life. The approach to measurement and identification of noise sources needs to be improved and enhanced to reduce high costs. Long measurement times and the need for expensive equipment and trained personnel must be automated. Simplifying the identification of main noise sources and excluding residual and background noise allows more effective measures. By spatially filtering the acoustic scene and combining unsupervised learning with psychoacoustic features, this paper presents a prototype system capable of automated calculation of the contribution of individual noise sources to the total noise level. Pilot measurements were performed at three different locations in the city of Ljubljana, Slovenia. Equivalent sound pressure levels obtained with the device were compared to the results obtained by manually marking individual parts of each of the three measurements. The proposed approach correctly identified the main noise sources in the vicinity of the measurement points.

Abstract This paper presents a new dataset of measured multichannel room impulse responses (RIRs) named dEchorate. It includes annotations of early echo timings and 3D positions of microphones, real sources, and image sources under different wall configurations in a cuboid room. These data provide a tool for benchmarking recent methods in echo-aware speech enhancement, room geometry estimation, RIR estimation, acoustic echo retrieval, microphone calibration, echo labeling, and reflector position estimation. The dataset is provided with software utilities to easily access, manipulate, and visualize the data as well as baseline methods for echo-related tasks.

Steady-State Visual Evoked Potential (SSVEP) recognition methods use a subject's calibration data to differentiate between brain responses, hence, providing the SSVEP-based brain–computer interfaces (BCIs) with high performance. However, they require sufficient calibration EEG trials to achieve that. This study develops a new method to learn from limited calibration EEG trials, and it proposes and evaluates a novel adaptive data-driven spatial filtering approach for enhancing SSVEP detection. The spatial filter learned from each stimulus utilizes temporal information from the corresponding EEG trials. To introduce the temporal information into the overall procedure, a multitask learning approach, based on the Bayesian framework, is adopted. The performance of the proposed method was evaluated into two publicly available benchmark datasets, and the results demonstrated that our method outperformed competing methods by a significant margin. [ABSTRACT FROM AUTHOR]

Simple Summary: With the development of artificial intelligence, researchers can roughly predict the crystal structure of a protein by computer without the need for biological experiments, which provides new ideas and solutions to problems, such as protein-protein interaction and drug-target predictions. In this study, we proposed strategies to combine predicted protein structures with deep learning networks and evaluated them on different protein binding site prediction tasks. Our computational experiment results showed that all proposed strategies could effectively encode structural information for deep learning models. Though AlphaFold2 has attained considerably high precision on protein structure prediction, it is reported that directly inputting coordinates into deep learning networks cannot achieve desirable results on downstream tasks. Thus, how to process and encode the predicted results into effective forms that deep learning models can understand to improve the performance of downstream tasks is worth exploring. In this study, we tested the effects of five processing strategies of coordinates on two single-sequence protein binding site prediction tasks. These five strategies are spatial filtering, the singular value decomposition of a distance map, calculating the secondary structure feature, and the relative accessible surface area feature of proteins. The computational experiment results showed that all strategies were suitable and effective methods to encode structural information for deep learning models. In addition, by performing a case study of a mutated protein, we showed that the spatial filtering strategy could introduce structural changes into HHblits profiles and deep learning networks when protein mutation happens. In sum, this work provides new insight into the downstream tasks of protein-molecule interaction prediction, such as predicting the binding residues of proteins and estimating the effects of mutations. [ABSTRACT FROM AUTHOR]

As pixel resolution continues to increase in images acquired from different sensors, the measurement of the correlation between pixels and their properties requires numerous processing steps over multiple iterations. Quantum image processing (QIP) is a technology capable of processing exponentially large volumes of data within polynomial time. One such application is image fusion. In the field of radiography, computerized tomography (CT) images provide the structural information of bone tissues, and magnetic resonance MR images can provide the visualization of gray‐scale anatomical structures of soft tissues. With the help of CT and MR fusion, a composite image can be obtained containing information on both hard and soft tissues. Clinicians often have to switch between these modalities to trace various patterns for effective staging and diagnosis of various oncological diseases. In this study, a multilevel filtering‐based image fusion algorithm that efficiently fuses information from CT and MR images into a single image has been proposed. First, source images are processed with a rolling guidance filter, and the detail layer is calculated. The base layer is then computed using guided filtering to ensure a high level of edge preservation. Furthermore, to minimize noise and artifacts, the detail layers are fused using the Karhunen–Loeve transform, and the base layers are combined using the weighted superimposition principle. Experimental results demonstrate that the proposed methodology is subjectively more efficient than the existing state‐of‐the‐art methods. [ABSTRACT FROM AUTHOR]

Population spatialization is widely used for spatially downscaling census population data to finer-scale. The core idea of modern population spatialization is to establish the association between ancillary data and population at the administrative-unit-level (AU-level) and transfer it to generate the gridded population. However, the statistical characteristic of attributes at the pixel-level differs from that at the AU-level, thus leading to prediction bias via the cross-scale modeling (i.e. scale mismatch problem). In addition, integrating multi-source data simply as covariates may underutilize spatial semantics, and lead to incorrect population disaggregation; while neglecting the spatial autocorrelation of population generates excessively heterogeneous population distribution that contradicts to real-world situation. To address the scale mismatch in downscaling, this paper proposes a Cross-Scale Feature Construction (CSFC) method. More specifically, by grading pixel-level attributes, we construct the feature vector of pixel grade proportions to narrow the scale differences in feature representation between AU-level and pixel-level. Meanwhile, fine-grained building patch and mobile positioning data are utilized to adjust the population weighting layer generated from POI-density-based regression modeling. Spatial filtering is furtherly adopted to model the spatial autocorrelation effect of population and reduce the heterogeneity in population caused by pixel-level attribute discretization. Through the comparison with traditional feature construction method and the ablation experiments, the results demonstrate significant accuracy improvements in population spatialization and verify the effectiveness of weight correction steps. Furthermore, accuracy comparisons with WorldPop and GPW datasets quantitatively illustrate the advantages of the proposed method in fine-scale population spatialization. [ABSTRACT FROM AUTHOR]

Hyperspectral anomaly detection (HAD) is an important application of hyperspectral images (HSI) that can distinguish anomalies from background in an unsupervised manner. As a common unsupervised network in deep learning, autoencoders (AE) have been widely used in HAD and can highlight anomalies by reconstructing the background. This study proposed a novel spatial–spectral joint HAD method based on a two-branch 3D convolutional autoencoder and spatial filtering. We used the two-branch 3D convolutional autoencoder to fully extract the spatial–spectral joint features and spectral interband features of HSI. In addition, we used a morphological filter and a total variance curvature filter for spatial detection. Currently, most of the datasets used to validate the performance of HAD methods are airborne HSI, and there are few available satellite-borne HSI. For this reason, we constructed a dataset of satellite-borne HSI based on the GF-5 satellite for experimental validation of our anomaly detection method. The experimental results for the airborne and satellite-borne HSI demonstrated the superior performance of the proposed method compared with six state-of-the-art methods. The area under the curve (AUC) values of our proposed method on different HSI reached above 0.9, which is higher than those of the other methods.

INTRODUCTION: In image processing noise removal is a hot research field. Lots of studies have been carried out and many algorithms and filters have been planned to improve the image information. There are various noise removal procedures to identify and remove the corrupted pixels. But several image de-noising algorithms apply the filter to the overall image to filter non-corrupted pixels also. To overcome these weaknesses, we proposed an efficient denoising algorithm by cascading Adaptive Median Filter (AMF) with Modified Decision Based Median Filter (MDBMF). OBJECTIVES: To acquire an efficient denoising algorithm for impulse noise reduction by combining AMF and MDBMF methods which are effective, efficient for denoising various kinds of images. To retain the edges, prevent signal deterioration, and ensure non-corrupted image pixels are remaining intact, regardless of various degrees of noise in the image. To avoid the condition where noisy pixels are replaced by other noisy pixels to maintain the quality of images from its degraded noise version such as blur which often takes place during transmission, acquisition, storage, etc. METHODS, RESULTS AND CONCLUSION: The performance corroboration of the proposed efficient denoising algorithmis accomplished employing nine standard grayscale images. The size of all standard images kept 256x256 pixels in this research. The proposed image denoising system has experimented on those images affected with 10% to 90% salt & pepper noise density. Additionally, the performance of the existing state-of-art denoising techniques like AMF, MF, WMF, UMF, and DBMF are contrasted with the proposed hybrid approach. The results showed that de-noised images obtained for 10% to 90% densities levels by proposed hybrid approach are quite better than the quality of denoised images achieved from WMF, UTMF, AMF, and DBMF methods. The proposed algorithm effectively eradicates salt and pepper noise for lower to higher image noise densities levels. [ABSTRACT FROM AUTHOR]

This paper presents a novel and robust algorithm to estimate the direction of arrival of sources by combining spatial time–frequency distributions and spatial filtering. The proposed algorithm first analyzes a given signal using robust spatial time–frequency distribution. Then, the strongest time–frequency points are selected and clustered to find out the direction of the strongest source. The signal corresponding to the strongest source is then removed through spatial filtering, and this process is iterated till the directions of all the sources are estimated. Numerical results indicate that the proposed method achieves better performance compared to other state-of-the-art methods for both complete signals and sparsely sampled signals. For example, in direction of arrival estimation, the proposed method achieves a mean square error of - 37 dB which is 2 dB lower than the mean square error achieved by the next best method. [ABSTRACT FROM AUTHOR]

Biomedical imaging has helped the medical practitioners to facilitate treatments faster and more accurate than conventional methods. Automated technologies suffice the default preprocessing and identification from the images of the retina and their vessel vascular network. Human retinal diseases, leading to blindness, will be identified by the segmentation of blood vessels to enhance the statistical accuracy of intra-retinal layer thickness. This proposal overcomes the drawbacks of traditional angiogram techniques by coloring the retinal paths by fluorescent dyes. From the obtained results, automated segmentation by using the proposed technique has improved then compared methodologies. Employing Gabor filters in the preprocessing functions, elimination of false positives is greatly achieved with some modifications. The followed processes divide the retinal images into linear and curvature segments after implying Gabor constraints to obtain a predicted trajectory of vessels. Limiting the major chances of false positives and better vessel construction in this automated proposal are proved and discussed. [ABSTRACT FROM AUTHOR]

Since anomaly targets in hyperspectral images (HSIs) with high spatial resolution appear as connected areas instead of single pixels or subpixels, both spatial and spectral information of HSIs can be exploited for a hyperspectal anomaly detection (AD) task. This article proposes a hyperspectral AD method based on Wasserstein distance (WD) and spatial filtering (called AD-WDSF). Based on the assumption that both background and anomaly targets obey the multivariate Gaussian distribution, background and anomaly target distributions are estimated in the local regions of HSIs. Subsequently, the anomaly intensity of test pixels centered in the local regions are determined via measuring the WD between background and anomaly target distributions. Lastly, spatial filters, i.e., guided filter (GF), total variation curvature filter (TVCF), and Maxtree filter, are exploited to further refine detection results. Experimental results conducted on two real hyperspectral data sets demonstrate that the proposed method achieves competitive detection performance compared with the state-of-the-art AD methods. [ABSTRACT FROM AUTHOR]

With the increase in mobility in technology, artists are looking for ways to create paintings using their tablets and smartphones while on the move. Digital oil painting is a revolutionary technique where an artist uses digital paintbrush to simulate the traditional bristle paintbrush of a painting to get an oil paint image. In this article, we propose a novel realistic technique for reversible digital oil painting to accelerate the users to (re)paint an oil paint image to convey the surrounding atmosphere, express their emotions and feelings, or showcase a time of day or type of weather through that painting. Realism is all about accuracy. To repaint, the original image is retrieved from the existing oil paint image by reversing the pixels to its original value. We derive a mathematical expression to determine the probability of a pixel, with a set of neighbor pixels, being oil painted to get an oil paint image of the desired quality, which reduces the time and space complexities of the technique. We also develop prototypes on Android, Windows, and Ubuntu operating systems that successfully implement the proposed technique. [ABSTRACT FROM AUTHOR]

Rehabilitation training based on the brain-computer interface of motor imagery (MI-BCI) can help restore the connection between the brain and movement. However, the performance of most popular MI-BCI system is coarse-level, which means that they are good at guiding the rehabilitation exercises of different parts of the body, but not for the individual component. In this paper, we designed a fine-level MI-BCI system for unilateral upper limb rehabilitation assistance. Besides, due to the low discrimination of different sample classes in a single part, a classification algorithm called spatial-temporal filtering convolutional network (STFCN) was proposed that used spatial filtering and deep learning. Our STFCN outperforms popular methods in recent years using BCI IV 2a and 2b data sets. To verify the effectiveness of our system, we recruited 6 volunteers and collected their data for a four-classification online experiments, resulting in an average accuracy of 62.7 %. This fine-level MI-BCI system has good appli-cation prospects, and inspires more exploration of rehabilitation in a single part of the human body. • Developed a fine-grained MI-BCI system for unilateral upper limbs with four distinct classes. • Introduced a novel EEG classification algorithm combining spatial filtering and deep learning. • Enhanced decoding performance by utilizing both temporal and spatial EEG characteristics. • Validated MI-BCI system through offline and online experiments with six recruited subjects. [ABSTRACT FROM AUTHOR]

Abstract Maximum spatial eigenfiltering improves the accuracy of maximum likelihood direction-of-arrival estimators for closely-spaced signal sources but may interchangeably attenuate widely-spaced signal sources, producing a severe performance degradation. Although this behavior has been observed experimentally, it still lacks a mathematical explanation. In our previous work, we overcame these limitations using a differential spectrum-based spatial filter but this still caused a small degradation in the DOA estimate. In this paper, we develop a mathematical analysis of how the signal source separation and the Karhunen-Loève expansion affect the passbands of the maximum spatial eigenfilter. The farther the sources, the less significant is the maximum eigenvalue of the spatial correlation matrix and its corresponding eigenvector. Then, the magnitude response of the maximum spatial eigenfilter no longer approximates the spatial power spectrum and is not guaranteed to place multiple passbands around the signal sources. Consequently, we propose a spatial filter built from the eigenvectors of the entire signal subspace. This filter showed an overall runtime smaller than that of our previous work. It also provides a significant reduction in the threshold signal-to-noise ratio for closely-spaced signal sources and does not hamper the estimation for widely-spaced signal sources.